The next are the report’s key findings:

Businesses buy into AI/ML, but struggle to scale across the organization. The overwhelming majority (93%) of respondents have several experimental or in-use AI/ML projects, with larger firms more likely to have greater deployment. A majority (82%) say ML investment will increase throughout the next 18 months, and closely tie AI and ML to revenue goals. Yet scaling is a significant challenge, as is hiring expert staff, finding appropriate use cases, and showing value.

Deployment success requires a talent and skills strategy. The challenge goes further than attracting core data scientists. Firms need hybrid and translator talent to guide AI/ML design, testing, and governance, and a workforce technique to ensure all users play a job in technology development. Competitive firms should offer clear opportunities, progression, and impacts for staff that set them apart. For the broader workforce, upskilling and engagement are key to support AI/ML innovations.

Centers of excellence (CoE) provide a foundation for broad deployment, balancing technology-sharing with tailored solutions. Corporations with mature capabilities, often larger firms, are likely to develop systems in-house. A CoE provides a hub-and-spoke model, with core ML consulting across divisions to develop widely deployable solutions alongside bespoke tools. ML teams needs to be incentivized to remain abreast of rapidly evolving AI/ML data science developments.

AI/ML governance requires robust model operations, including data transparency and provenance, regulatory foresight, and responsible AI. The intersection of multiple automated systems can bring increased risk, resembling cybersecurity issues, illegal discrimination, and macro volatility, to advanced data science tools. Regulators and civil society groups are scrutinizing AI that affects residents and governments, with special attention to systemically essential sectors. Corporations need a responsible AI strategy based on full data provenance, risk assessment, and checks and controls. This requires technical interventions, resembling automated flagging for AI/ML model faults or risks, in addition to social, cultural, and other business reforms.